class_docstr: Augmenter that apply randomly behavior for augmentation.
  
  :param str action: 'substitute', 'swap', 'delete' or 'crop'. If value is 'swap', adjacent words will be swapped randomly.
      If value is 'delete', word will be removed randomly. If value is 'crop', a set of contunous word will be removed randomly.
  :param float aug_p: Percentage of word will be augmented. 
  :param int aug_min: Minimum number of word will be augmented.
  :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from
      aug_p. Otherwise, using aug_max.
  :param list stopwords: List of words which will be skipped from augment operation. Not effective if action is 'crop'
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation. Not effective if action is 'crop'
  :param list target_words: List of word for replacement (used for substitute operation only). Default value is _.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.RandomWordAug()
code: def __init__(
      self,
      action=Action.DELETE,
      name="RandomWord_Aug",
      aug_min=1,
      aug_max=10,
      aug_p=0.3,
      stopwords=None,
      target_words=None,
      tokenizer=None,
      reverse_tokenizer=None,
      stopwords_regex=None,
      verbose=0,
  ):
      super().__init__(
          action=action,
          name=name,
          aug_p=aug_p,
          aug_min=aug_min,
          aug_max=aug_max,
          stopwords=stopwords,
          tokenizer=tokenizer,
          reverse_tokenizer=reverse_tokenizer,
          device="cpu",
          verbose=verbose,
          stopwords_regex=stopwords_regex,
          include_detail=False,
      )
  
      self.target_words = target_words or ["_"]
  
file_path: nlpaug/nlpaug/augmenter/word/random.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 1 to create a new RandomWordAug object from inherited base classes: WordAugmenter with example: obj = RandomWordAug(action, name, aug_min, aug_max, aug_p, stopwords, target_words, tokenizer, reverse_tokenizer, stopwords_regex, verbose)